Virtual Reality Simulation of Autonomous Solar Plants Inspections with Unmanned Aerial Systems

Andrade, Fabio; Sivertsen, Agnar Holten; Correia, Carlos Alberto; Belbachir, Nabil; Sousa, Lucas Costa De; Rufino, Victor Muller Pereira; Petropolis, Eduardo Pimenta; Silva, Erick Rodrigues e; Henriques, Victor Hugo Rinaldi Fortes

doi:10.1109/airpharo52252.2021.9571060

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…These acquisition devices can be RGB, infrared or hyperspectral cameras, for example. There are a variety of applications using such sensors, e.g., in the fields of inspection [1], mapping [2], search and rescue [3], tracking [4], border patrol [5], sea surveillance [6], agriculture [7], and even recreation [8].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications

Correia

Andrade

Sivertsen

et al. 2022

Sensors

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Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications

Correia

Andrade

Sivertsen

et al. 2022

Sensors

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Immersive Extended Reality (XR) Technology in Engineering Education: Opportunities and Challenges

Khlaif,

Mousa,

Sanmugam

2024

Tech Know Learn

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A Novel Framework to Evaluate and Train Object Detection Models for Real-Time Victims Search and Rescue at Sea with Autonomous Unmanned Aerial Systems Using High-Fidelity Dynamic Marine Simulation Environment

Poudel

Lima

Andrade

2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)

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This work presents a novel framework providing the ability to control an Unmanned Aerial System (UAS) while detecting objects in real-time with visible detections, containing class names, bounding boxes, and confidence scores, in a changeable high-fidelity sea simulation environment, where the major attributes like the number of human victims and debris floating, ocean waves and shades, weather conditions such as rain, snow, and fog, sun brightness and intensity, camera exposure and brightness can easily be manipulated. Developed using Unreal Engine, Microsoft Air-Sim, and Robot Operating System (ROS), the framework was firstly used to find the best possible configuration of the UAS flight altitude, and camera brightness with high average prediction confidence of human victim detection, and then only autonomous real-time test missions were carried out to calculate the accuracies of two pretrained You Only Look Once Version 7 (YOLOv7) models: YOLOv7 retrained on SeaDronesSee Dataset (YOLOv7-SDS) and YOLOv7 originally trained on Microsoft COCO Dataset (YOLOv7-COCO), which resulted in high values of 97.8% and 93.79%, respectively. Furthermore, it is proposed that the framework developed in this study can be reverse engineered for autonomous real-time training with automatic ground-truth labeling of the images from the gaming engine that already has all the details of all objects placed in the environment for rendering them onto the screen. This is required to be done to avoid the cumbersome and timeconsuming manual labeling of large amount of synthetic data that can be extracted using this framework which could be a groundbreaking achievement in the field of maritime computer vision.

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Virtual Reality Simulation of Autonomous Solar Plants Inspections with Unmanned Aerial Systems

Cited by 3 publications

References 11 publications

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications

Immersive Extended Reality (XR) Technology in Engineering Education: Opportunities and Challenges

A Novel Framework to Evaluate and Train Object Detection Models for Real-Time Victims Search and Rescue at Sea with Autonomous Unmanned Aerial Systems Using High-Fidelity Dynamic Marine Simulation Environment

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